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274 lines
11 KiB
274 lines
11 KiB
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. |
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# |
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# Licensed under the Apache License, Version 2.0 (the "License"); |
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# you may not use this file except in compliance with the License. |
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# You may obtain a copy of the License at |
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# |
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# http://www.apache.org/licenses/LICENSE-2.0 |
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# |
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# Unless required by applicable law or agreed to in writing, software |
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# distributed under the License is distributed on an "AS IS" BASIS, |
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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# See the License for the specific language governing permissions and |
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# limitations under the License. |
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import os.path as osp |
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import tempfile |
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import paddlers as pdrs |
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import paddlers.transforms as T |
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from testing_utils import CommonTest |
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class TestSegSliderPredict(CommonTest): |
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def setUp(self): |
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self.model = pdrs.tasks.seg.UNet(in_channels=10) |
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self.transforms = T.Compose([ |
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T.DecodeImg(), T.Normalize([0.5] * 10, [0.5] * 10), |
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T.ArrangeSegmenter('test') |
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]) |
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self.image_path = "data/ssst/multispectral.tif" |
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self.basename = osp.basename(self.image_path) |
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def test_blocksize_and_overlap_whole(self): |
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# Original image size (256, 256) |
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with tempfile.TemporaryDirectory() as td: |
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# Whole-image inference using predict() |
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pred_whole = self.model.predict(self.image_path, self.transforms) |
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pred_whole = pred_whole['label_map'] |
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# Whole-image inference using slider_predict() |
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save_dir = osp.join(td, 'pred1') |
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self.model.slider_predict(self.image_path, save_dir, 256, 0, |
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self.transforms) |
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pred1 = T.decode_image( |
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osp.join(save_dir, self.basename), |
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to_uint8=False, |
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decode_sar=False) |
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self.check_output_equal(pred1.shape, pred_whole.shape) |
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# `block_size` == `overlap` |
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save_dir = osp.join(td, 'pred2') |
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with self.assertRaises(ValueError): |
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self.model.slider_predict(self.image_path, save_dir, 128, 128, |
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self.transforms) |
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# `block_size` is a tuple |
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save_dir = osp.join(td, 'pred3') |
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self.model.slider_predict(self.image_path, save_dir, (128, 32), 0, |
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self.transforms) |
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pred3 = T.decode_image( |
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osp.join(save_dir, self.basename), |
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to_uint8=False, |
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decode_sar=False) |
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self.check_output_equal(pred3.shape, pred_whole.shape) |
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# `block_size` and `overlap` are both tuples |
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save_dir = osp.join(td, 'pred4') |
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self.model.slider_predict(self.image_path, save_dir, (128, 100), |
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(10, 5), self.transforms) |
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pred4 = T.decode_image( |
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osp.join(save_dir, self.basename), |
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to_uint8=False, |
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decode_sar=False) |
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self.check_output_equal(pred4.shape, pred_whole.shape) |
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# `block_size` larger than image size |
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save_dir = osp.join(td, 'pred5') |
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self.model.slider_predict(self.image_path, save_dir, 512, 0, |
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self.transforms) |
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pred5 = T.decode_image( |
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osp.join(save_dir, self.basename), |
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to_uint8=False, |
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decode_sar=False) |
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self.check_output_equal(pred5.shape, pred_whole.shape) |
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def test_merge_strategy(self): |
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with tempfile.TemporaryDirectory() as td: |
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# Whole-image inference using predict() |
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pred_whole = self.model.predict(self.image_path, self.transforms) |
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pred_whole = pred_whole['label_map'] |
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# 'keep_first' |
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save_dir = osp.join(td, 'keep_first') |
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self.model.slider_predict( |
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self.image_path, |
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save_dir, |
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128, |
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64, |
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self.transforms, |
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merge_strategy='keep_first') |
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pred_keepfirst = T.decode_image( |
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osp.join(save_dir, self.basename), |
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to_uint8=False, |
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decode_sar=False) |
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self.check_output_equal(pred_keepfirst.shape, pred_whole.shape) |
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# 'keep_last' |
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save_dir = osp.join(td, 'keep_last') |
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self.model.slider_predict( |
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self.image_path, |
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save_dir, |
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128, |
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64, |
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self.transforms, |
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merge_strategy='keep_last') |
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pred_keeplast = T.decode_image( |
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osp.join(save_dir, self.basename), |
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to_uint8=False, |
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decode_sar=False) |
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self.check_output_equal(pred_keeplast.shape, pred_whole.shape) |
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# 'vote' |
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save_dir = osp.join(td, 'vote') |
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self.model.slider_predict( |
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self.image_path, |
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save_dir, |
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128, |
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64, |
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self.transforms, |
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merge_strategy='vote') |
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pred_vote = T.decode_image( |
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osp.join(save_dir, self.basename), |
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to_uint8=False, |
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decode_sar=False) |
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self.check_output_equal(pred_vote.shape, pred_whole.shape) |
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def test_geo_info(self): |
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with tempfile.TemporaryDirectory() as td: |
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_, geo_info_in = T.decode_image(self.image_path, read_geo_info=True) |
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self.model.slider_predict(self.image_path, td, 128, 0, |
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self.transforms) |
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_, geo_info_out = T.decode_image( |
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osp.join(td, self.basename), read_geo_info=True) |
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self.assertEqual(geo_info_out['geo_trans'], |
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geo_info_in['geo_trans']) |
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self.assertEqual(geo_info_out['geo_proj'], geo_info_in['geo_proj']) |
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class TestCDSliderPredict(CommonTest): |
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def setUp(self): |
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self.model = pdrs.tasks.cd.BIT(in_channels=10) |
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self.transforms = T.Compose([ |
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T.DecodeImg(), T.Normalize([0.5] * 10, [0.5] * 10), |
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T.ArrangeChangeDetector('test') |
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]) |
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self.image_paths = ("data/ssmt/multispectral_t1.tif", |
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"data/ssmt/multispectral_t2.tif") |
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self.basename = osp.basename(self.image_paths[0]) |
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def test_blocksize_and_overlap_whole(self): |
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# Original image size (256, 256) |
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with tempfile.TemporaryDirectory() as td: |
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# Whole-image inference using predict() |
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pred_whole = self.model.predict(self.image_paths, self.transforms) |
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pred_whole = pred_whole['label_map'] |
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# Whole-image inference using slider_predict() |
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save_dir = osp.join(td, 'pred1') |
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self.model.slider_predict(self.image_paths, save_dir, 256, 0, |
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self.transforms) |
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pred1 = T.decode_image( |
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osp.join(save_dir, self.basename), |
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to_uint8=False, |
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decode_sar=False) |
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self.check_output_equal(pred1.shape, pred_whole.shape) |
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# `block_size` == `overlap` |
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save_dir = osp.join(td, 'pred2') |
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with self.assertRaises(ValueError): |
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self.model.slider_predict(self.image_paths, save_dir, 128, 128, |
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self.transforms) |
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# `block_size` is a tuple |
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save_dir = osp.join(td, 'pred3') |
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self.model.slider_predict(self.image_paths, save_dir, (128, 32), 0, |
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self.transforms) |
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pred3 = T.decode_image( |
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osp.join(save_dir, self.basename), |
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to_uint8=False, |
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decode_sar=False) |
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self.check_output_equal(pred3.shape, pred_whole.shape) |
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# `block_size` and `overlap` are both tuples |
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save_dir = osp.join(td, 'pred4') |
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self.model.slider_predict(self.image_paths, save_dir, (128, 100), |
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(10, 5), self.transforms) |
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pred4 = T.decode_image( |
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osp.join(save_dir, self.basename), |
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to_uint8=False, |
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decode_sar=False) |
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self.check_output_equal(pred4.shape, pred_whole.shape) |
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# `block_size` larger than image size |
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save_dir = osp.join(td, 'pred5') |
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self.model.slider_predict(self.image_paths, save_dir, 512, 0, |
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self.transforms) |
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pred5 = T.decode_image( |
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osp.join(save_dir, self.basename), |
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to_uint8=False, |
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decode_sar=False) |
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self.check_output_equal(pred5.shape, pred_whole.shape) |
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def test_merge_strategy(self): |
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with tempfile.TemporaryDirectory() as td: |
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# Whole-image inference using predict() |
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pred_whole = self.model.predict(self.image_paths, self.transforms) |
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pred_whole = pred_whole['label_map'] |
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# 'keep_first' |
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save_dir = osp.join(td, 'keep_first') |
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self.model.slider_predict( |
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self.image_paths, |
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save_dir, |
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128, |
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64, |
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self.transforms, |
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merge_strategy='keep_first') |
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pred_keepfirst = T.decode_image( |
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osp.join(save_dir, self.basename), |
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to_uint8=False, |
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decode_sar=False) |
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self.check_output_equal(pred_keepfirst.shape, pred_whole.shape) |
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# 'keep_last' |
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save_dir = osp.join(td, 'keep_last') |
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self.model.slider_predict( |
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self.image_paths, |
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save_dir, |
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128, |
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64, |
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self.transforms, |
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merge_strategy='keep_last') |
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pred_keeplast = T.decode_image( |
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osp.join(save_dir, self.basename), |
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to_uint8=False, |
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decode_sar=False) |
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self.check_output_equal(pred_keeplast.shape, pred_whole.shape) |
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# 'vote' |
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save_dir = osp.join(td, 'vote') |
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self.model.slider_predict( |
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self.image_paths, |
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save_dir, |
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128, |
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64, |
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self.transforms, |
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merge_strategy='vote') |
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pred_vote = T.decode_image( |
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osp.join(save_dir, self.basename), |
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to_uint8=False, |
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decode_sar=False) |
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self.check_output_equal(pred_vote.shape, pred_whole.shape) |
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def test_geo_info(self): |
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with tempfile.TemporaryDirectory() as td: |
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_, geo_info_in = T.decode_image( |
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self.image_paths[0], read_geo_info=True) |
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self.model.slider_predict(self.image_paths, td, 128, 0, |
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self.transforms) |
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_, geo_info_out = T.decode_image( |
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osp.join(td, self.basename), read_geo_info=True) |
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self.assertEqual(geo_info_out['geo_trans'], |
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geo_info_in['geo_trans']) |
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self.assertEqual(geo_info_out['geo_proj'], geo_info_in['geo_proj'])
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